The mortgage industry forces operations teams to speed production, enhance throughput, and reduce cost per loan. However, for many mortgage companies, this is more easily said than done because of ineffective manual procedures that are costly, time-consuming, and prone to errors.
Fremont, CA: The mortgage industry forces operations teams to speed production, enhance throughput, and reduce cost per loan. However, for many mortgage companies, this is more easily said than done because of ineffective manual procedures that are costly, time-consuming, and prone to errors.
Data and the documents that served as its foundation must be examined and checked often throughout production to ensure accuracy. Operations executives may be confident in satisfying risk profiles and making informed lending decisions thanks to procedures for checking and rechecking. Manual data verification, on the other hand, necessitates experience and time. It quickly becomes expensive, so operations are constantly scrutinized as one of the cost centers.
Reducing the cost-per-loan
One of the most crucial indicators for any leader in mortgage operations is the cost per loan. Mortgage manufacturing costs have risen despite technological investments, such as loan origination systems (LOS) and robotic process automation (RPA). These systems or processes are not at the root of the issue. They are fed by the data, which is with. Garbage in, garbage out, as the saying goes.
Most clients have LOSs, while some have also made RPA investments. In either case, these operators require clean data to operate their systems and procedures properly. The value of automation efforts can ultimately be realized after a business has clean data.
Reduction of fault rates
People are imperfect. Even loan specialists with the highest education and expertise make mistakes while attempting to extract data from documents. Customers may need help initially accepting the AI's near-perfect accuracy in converting documents to data. Although there is a lot of understandable skepticism toward automation and AI, it takes great satisfaction in the continued excellent performance of our machine learning models. They think creatively and constantly strive to get better.
Enhancing the headcount
For our clients, identifying how to staff appropriately is a constant problem. So many factors influence both the labor availability and the incoming volumes. Many operations leaders use models to forecast throughput and balance it with staffing, overtime, and outsourcing requirements. The bottom line is affected when the models are inaccurate and they are challenging to get right.
Dealing with seasonality
Another difficulty with elasticity is the seasonality that dominates the mortgage industry. The business is unusually quiet because of a falling home market, rising mortgage rates, and the winter season. Operations managers must decide when and how much to increase staffing as spring approaches.
The quality of customer service and the brand's reputation are significantly influenced by staffing. It's harder than ever to adjust to seasonal and economic cycles because cost-per-loan measures are increasing, and hiring and outsourcing are getting harder.
